Large Language Models (LLMs) have enabled a wide range of applications through their powerful capabilities in language understanding and generation. However, as LLMs are trained on static corpora, they face difficulties in addressing rapidly evolving information or domain-specific queries. Retrieval-Augmented Generation (RAG) was developed to overcome this limitation by integrating LLMs with external retrieval mechanisms, allowing them to access up-to-date and contextually relevant knowledge. However, as LLMs themselves continue to advance in scale and capability, the relative advantages of traditional RAG frameworks have become less pronounced and necessary. Here, we present a comprehensive review of RAG, beginning with its overarching objectives and core components. We then analyze the key challenges within RAG, highlighting critical weakness that may limit its effectiveness. Finally, we showcase applications where LLMs alone perform inadequately, but where RAG, when combined with LLMs, can substantially enhance their effectiveness. We hope this work will encourage researchers to reconsider the role of RAG and inspire the development of next-generation RAG systems.

翻译：大型语言模型（LLMs）凭借其在语言理解与生成方面的强大能力，已赋能广泛的应用场景。然而，由于LLMs基于静态语料库进行训练，它们在处理快速演进的信息或领域特定查询时面临困难。检索增强生成（RAG）通过将LLMs与外部检索机制相结合而发展起来，使其能够获取最新且与上下文相关的知识，从而克服了这一局限。然而，随着LLMs自身在规模与能力上的持续进步，传统RAG框架的相对优势已变得不再显著且必要性降低。本文对RAG进行了全面综述，首先阐述其总体目标与核心组件，继而分析RAG中的关键挑战，着重指出可能限制其效能的突出弱点。最后，我们展示了仅靠LLMs表现不足、但结合RAG后能显著提升其效能的若干应用场景。我们希望这项工作能促使研究者重新审视RAG的角色，并激发下一代RAG系统的开发。